Pythia Generated Jet Images With Alternative Rotation Scheme For Location Aware Generative Adversarial Network Training
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下载链接：
https://zenodo.org/record/268592
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资源简介：
Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927].
<strong>Format</strong>:
HDF5 file with the following fields:
'image' : array of dim (300000, 25, 25), contains the pixel intensities of each 25x25 image
'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)
'jet_eta': eta coordinate per jet
'jet_phi': phi coordinate per jet
'jet_mass': mass per jet
'jet_pt': transverse momentum per jet
'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0
'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)
'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet
'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet
<strong>Details</strong>:
Simulated using Pythia 8.219 at √ s = 14 TeV
Image pre-processing using method from in L. de Oliveira et al., <em>Jet-Images -- Deep Learning Edition </em>[arXiv:1511.05190]
scikit-image==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0
Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]
Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets
Intensity of pixel = p<sub>T</sub> of cell
60 GeV < m<sup>jet</sup> < 100 GeV
250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV
Sparse images (~10% NNZ)
Full dataset description in [arXiv:1701.05927].
提供机构：
Zenodo创建时间：
2017-02-04
